Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Conventional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors as reference points. Those algorithms usually produce results that accurately reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the inputs and use it to synthesize plausible images. While those methods may sometimes produce impressively sharp outputs, they may not always faithfully reproduce the content of the latent image.